Roberto R Herai1, Lisa Stefanacci2, Branka Hrvoj-Mihic2, Thanathom Chailangkarn1, Kari Hanson2, Katerina Semendeferi2,3,4, Alysson R Muotri1,3,4. 1. University of California San Diego, School of Medicine, Department of Pediatrics/Rady Children's Hospital San Diego, Department of Cellular & Molecular Medicine, Stem Cell Program, La Jolla, CA 92093, MC 0695, USA. 2. University of California San Diego, Department of Anthropology, 9500 Gilman Drive, La Jolla, CA, 92093, USA. 3. Center for Academic Research and Training in Anthropogeny (CARTA), University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093. USA. 4. Neuroscience Graduate Program, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.
Abstract
BACKGROUND: Formalin fixation (FF) is the standard and most common method for preserving postmortem brain tissue. FF stabilizes cellular morphology and tissue architecture, and can be used to study the distinct morphologic and genetic signatures of different cell types. Although the procedure involved in FF degrades messenger RNA over time, an alternative approach is to use small RNAs (sRNAs) for genetic analysis associated with cell morphology. Although genetic analysis is carried out on fresh or frozen tissue, there is limited availability or impossibility on targeting specific cell populations, respectively. NEW METHOD: The goal of this study is to detect miRNA and other classes of sRNA stored in formalin or in paraffin embedded for over decades. Two brain samples, one formed by a mixed population of cortical and subcortical cells, and one formed by pyramidal shaped cells collected by laser-capture microdissection, were subjected to sRNA sequencing. RESULTS: Performing bioinformatics analysis over the sequenced sRNA from brain tissue, we detected several classes of sRNA, such as miRNAs that play key roles in brain neurodevelopmental and maintenance pathways, and hsa-mir-155 expression in neurons. Comparison with existing method: Our method is the first to combine the approaches for: laser-capture of pyramidal neurons from long-term formalin-fixed brain; extract sRNA from laser-captured pyramidal neurons; apply a suite of bioinformatics tools to detect miRNA and other classes of sRNAs on sequenced samples having high levels of RNA degradation. CONCLUSION: This is the first study to show that sRNA can be rescued from laser-captured FF pyramidal neurons.
BACKGROUND:Formalin fixation (FF) is the standard and most common method for preserving postmortem brain tissue. FF stabilizes cellular morphology and tissue architecture, and can be used to study the distinct morphologic and genetic signatures of different cell types. Although the procedure involved in FF degrades messenger RNA over time, an alternative approach is to use small RNAs (sRNAs) for genetic analysis associated with cell morphology. Although genetic analysis is carried out on fresh or frozen tissue, there is limited availability or impossibility on targeting specific cell populations, respectively. NEW METHOD: The goal of this study is to detect miRNA and other classes of sRNA stored in formalin or in paraffin embedded for over decades. Two brain samples, one formed by a mixed population of cortical and subcortical cells, and one formed by pyramidal shaped cells collected by laser-capture microdissection, were subjected to sRNA sequencing. RESULTS: Performing bioinformatics analysis over the sequenced sRNA from brain tissue, we detected several classes of sRNA, such as miRNAs that play key roles in brain neurodevelopmental and maintenance pathways, and hsa-mir-155 expression in neurons. Comparison with existing method: Our method is the first to combine the approaches for: laser-capture of pyramidal neurons from long-term formalin-fixed brain; extract sRNA from laser-captured pyramidal neurons; apply a suite of bioinformatics tools to detect miRNA and other classes of sRNAs on sequenced samples having high levels of RNA degradation. CONCLUSION: This is the first study to show that sRNA can be rescued from laser-captured FF pyramidal neurons.
Authors: Jeffrey Hsu; Peter Hanna; David R Van Wagoner; John Barnard; David Serre; Mina K Chung; Jonathan D Smith Journal: Circ Cardiovasc Genet Date: 2012-04-03
Authors: Nicole Barger; Lisa Stefanacci; Cynthia M Schumann; Chet C Sherwood; Jacopo Annese; John M Allman; Joseph A Buckwalter; Patrick R Hof; Katerina Semendeferi Journal: J Comp Neurol Date: 2012-09-01 Impact factor: 3.215
Authors: Z S Walters; B Villarejo-Balcells; D Olmos; T W S Buist; E Missiaglia; R Allen; B Al-Lazikani; M D Garrett; J Blagg; J Shipley Journal: Oncogene Date: 2013-02-25 Impact factor: 9.867
Authors: Febe van Maldegem; Mireille de Wit; Folkert Morsink; Alex Musler; Jitske Weegenaar; Carel J M van Noesel Journal: Diagn Mol Pathol Date: 2008-03
Authors: Andrew D Gaudet; Shweta Mandrekar-Colucci; Jodie C E Hall; David R Sweet; Philipp J Schmitt; Xinyang Xu; Zhen Guan; Xiaokui Mo; Mireia Guerau-de-Arellano; Phillip G Popovich Journal: J Neurosci Date: 2016-08-10 Impact factor: 6.167